1 About

Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.

2 Citation

Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom

3 Introduction

Background blurb about emissions, retofit, carbon tax/levy etc

4 Emissions Levy Case Study - All LSOAs

In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.

We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.

We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required.

It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA hetergeneity in emissions and so will almost certaonly underestimate the range of the household level emissions levy value.

NB: no maps in the interests of speed

4.1 Data

We will use a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).

All analysis is at LSOA level. Cautions on inference from area level data apply.

4.2 CREDS place-based emmissions estimates

See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/

“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”

“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."

Source: https://www.carbon.place/

Notes:

  • Emissions are presented as per capita…
  • Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc
##    region nLSOAs mean_KgCo2ePerCap sd_KgCo2ePerCap
## 1: London    162          6386.852        1488.554

Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings

Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)

First check the n electricity meters logic…

##       LSOA11NM                 WD18NM nGasMeters nElecMeters epc_total
## 1: Newham 037A            Royal Docks       1034        1861      1700
## 2: Newham 022D         Plaistow South        835         941       661
## 3: Newham 030C     Canning Town North        830         818       439
## 4: Newham 012C Stratford and New Town        808         860       590
## 5: Newham 009D      Forest Gate South        801         939       637
## 6: Newham 031C     Canning Town South        791         806       587
##       LSOA11NM                 WD18NM nGasMeters nElecMeters epc_total
## 1: Newham 013G Stratford and New Town        731        6351      6350
## 2: Newham 037E            Royal Docks        574        3116      2900
## 3: Newham 037A            Royal Docks       1034        1861      1700
## 4: Newham 033B                Beckton        406        1686      1360
## 5: Newham 013E Stratford and New Town        154        1671      1470
## 6: Newham 034H     Canning Town South        191        1585      1470
##       LSOA11NM                 WD18NM nGasMeters nElecMeters epc_total
## 1: Newham 037A            Royal Docks       1034        1861      1700
## 2: Newham 022D         Plaistow South        835         941       661
## 3: Newham 030C     Canning Town North        830         818       439
## 4: Newham 012C Stratford and New Town        808         860       590
## 5: Newham 009D      Forest Gate South        801         939       637
## 6: Newham 031C     Canning Town South        791         806       587

Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.

There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.

Check that the assumption seems sensible…

Check for outliers - what might this indicate?

4.2.1 Estimate per dwelling emissions

We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.

## # Summary of per dwelling values
Table 4.1: Data summary
Name …[]
Number of rows 162
Number of columns 9
Key NULL
_______________________
Column type frequency:
numeric 9
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
CREDStotal_kgco2e_pdw 0 1 20459.41 5650.39 3587.62 16921.59 20949.11 24307.81 32968.22 ▁▂▇▇▂
CREDSgas_kgco2e2018_pdw 0 1 2231.99 678.71 139.82 1862.83 2323.19 2744.06 3622.65 ▁▂▇▇▃
CREDSelec_kgco2e2018_pdw 0 1 929.81 130.64 553.10 835.82 916.78 1002.95 1347.45 ▁▆▇▂▁
CREDSmeasuredHomeEnergy_kgco2e2018_pdw 0 1 3161.80 736.65 895.16 2730.02 3171.34 3730.70 4945.02 ▁▂▇▇▁
CREDSotherEnergy_kgco2e2011_pdw 0 1 160.29 65.09 17.41 118.72 154.48 193.10 451.65 ▃▇▃▁▁
CREDSallHomeEnergy_kgco2e2018_pdw 0 1 3322.09 736.66 912.57 2892.58 3316.06 3880.60 5088.34 ▁▁▇▇▂
CREDScar_kgco2e2018_pdw 0 1 1283.90 400.86 161.79 1048.16 1291.32 1540.35 2298.04 ▁▃▇▅▁
CREDSvan_kgco2e2018_pdw 0 1 152.66 166.81 22.91 83.23 119.16 168.13 1665.36 ▇▁▁▁▁
CREDSpersonalTransport_kgco2e2018_pdw 0 1 1436.55 446.06 203.93 1165.89 1445.35 1739.86 2959.22 ▁▆▇▂▁

Examine patterns of per dwelling emissions for sense.

4.2.1.1 All emissions

Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.

## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level all per dwelling emissions against IMD score

Figure 4.1: Scatter of LSOA level all per dwelling emissions against IMD score

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -5.5899, df = 160, p-value = 9.588e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5256456 -0.2666358
## sample estimates:
##        cor 
## -0.4042125
##    LSOA11CD            WD18NM          All_Tco2e_per_dw
##  Length:162         Length:162         Min.   : 3.588  
##  Class :character   Class :character   1st Qu.:16.922  
##  Mode  :character   Mode  :character   Median :20.949  
##                                        Mean   :20.459  
##                                        3rd Qu.:24.308  
##                                        Max.   :32.968
##     LSOA11CD            WD18NM All_Tco2e_per_dw
## 1: E01003575     Little Ilford         32.96822
## 2: E01033580       Royal Docks         32.62727
## 3: E01003555 Forest Gate South         32.02980
## 4: E01003561 Green Street East         31.15000
## 5: E01003621          Wall End         30.94382
## 6: E01003585        Manor Park         30.64313
##     LSOA11CD                 WD18NM All_Tco2e_per_dw
## 1: E01003633               West Ham         9.121524
## 2: E01033585     Canning Town South         9.114810
## 3: E01003488                 Boleyn         8.355817
## 4: E01003577          Little Ilford         7.456061
## 5: E01033577            Royal Docks         7.333633
## 6: E01033583 Stratford and New Town         3.587624

4.2.1.2 Home energy use

Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.

## Per dwelling T CO2e - gas emissions
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   139.8  1862.8  2323.2  2232.0  2744.1  3622.7
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level gas per dwelling emissions against IMD score

Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -2.3581, df = 160, p-value = 0.01958
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.32818766 -0.02991636
## sample estimates:
##        cor 
## -0.1832664

Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.

## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -2.4732, df = 160, p-value = 0.01444
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.33614037 -0.03884478
## sample estimates:
##        cor 
## -0.1918909

Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -2.4732, df = 160, p-value = 0.01444
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.33614037 -0.03884478
## sample estimates:
##        cor 
## -0.1918909
##                      RUC11 mean_gas_kgco2e mean_elec_kgco2e mean_other_energy_kgco2e
## 1: Urban major conurbation        2231.987         929.8142                 160.2869

Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 12.698, df = 160, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6222617 0.7777135
## sample estimates:
##       cor 
## 0.7084787
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.

Repeat for all home energy - includes estimates of emissions from oil etc

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 12.558, df = 160, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6174264 0.7745915
## sample estimates:
##      cor 
## 0.704546
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

How does the correlation look now?

4.2.1.3 Transport

We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)

Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level car use per dwelling emissions against IMD score

Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -3.2064, df = 160, p-value = 0.001623
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.38531035 -0.09512201
## sample estimates:
##        cor 
## -0.2457135
##                      RUC11 mean_car_kgco2e mean_van_kgco2e
## 1: Urban major conurbation        1283.895        152.6556

Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.

## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Scatter of LSOA level van use per dwelling emissions against IMD score

Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = 0.76693, df = 160, p-value = 0.4443
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.09455784  0.21273030
## sample estimates:
##        cor 
## 0.06052002

4.2.2 Impute EPC counts

In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…

Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.

## N EPCs
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   129.0   312.0   354.5   470.5   448.8  6350.0
## N elec meters
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   422.0   533.0   619.5   716.8   717.0  6351.0

Correlation between high % EPC F/G or A/B and deprivation?

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Now we need to convert the % to dwellings using the number of electricity meters (see above).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.3 Estimating the annual emissions levy

Case studies:

  • Annual carbon tax
  • Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity

BEIS/ETC Carbon ‘price’

EU carbon ‘price’

BEIS Carbon ‘Value’ https://www.gov.uk/government/publications/valuing-greenhouse-gas-emissions-in-policy-appraisal/valuation-of-greenhouse-gas-emissions-for-policy-appraisal-and-evaluation#annex-1-carbon-values-in-2020-prices-per-tonne-of-co2

  • based on a Marginal Abatement Cost (MAC)
  • 2021:
    • Low: £122/T
    • Central: £245/T <- use the central value for now
    • High: £367/T

Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)

4.2.3.1 Scenario 1: Central cost

The table below shows the overall £ GBP total for the case study area in £M.

## £m
##    nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1:    162        518.7582         55.51613             25.70856
## £m
##    region nLSOAs beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: London    162        518.7582         55.51613             25.70856

The table below shows the mean per dwelling value rounded to the nearest £10.

##    beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw beis_GBPtotal_c_energy_perdw
## 1:                  5010                       550                        230                          770

Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA revenue using BEIS central carbon price

Figure 4.7: £k per LSOA revenue using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA revenue using BEIS central carbon price

Figure 4.8: £k per LSOA revenue using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     879    4146    5133    5013    5955    8077
##     LSOA11CD    LSOA01NM            WD18NM CREDStotal_kgco2e_pdw beis_GBPtotal_c_perdw
## 1: E01003575 Newham 005B     Little Ilford              32968.22              8077.214
## 2: E01033580 Newham 037F       Royal Docks              32627.27              7993.682
## 3: E01003555 Newham 007B Forest Gate South              32029.80              7847.300
## 4: E01003561 Newham 007C Green Street East              31150.00              7631.750
## 5: E01003621 Newham 023C          Wall End              30943.82              7581.236
## 6: E01003585 Newham 004B        Manor Park              30643.13              7507.566
##     LSOA11CD    LSOA01NM                 WD18NM CREDStotal_kgco2e_pdw beis_GBPtotal_c_perdw
## 1: E01003633 Newham 020C               West Ham              9121.524             2234.7734
## 2: E01033585 Newham 034J     Canning Town South              9114.810             2233.1284
## 3: E01003488 Newham 019A                 Boleyn              8355.817             2047.1753
## 4: E01003577 Newham 005C          Little Ilford              7456.061             1826.7348
## 5: E01033577 Newham 037E            Royal Docks              7333.633             1796.7401
## 6: E01033583 Newham 013G Stratford and New Town              3587.624              878.9679

Figure ?? repeats the analysis but just for gas.

Anything unusual?

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via gas using BEIS central carbon price

Figure 4.9: £k per LSOA incurred via gas using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via gas using BEIS central carbon price

Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   34.25  456.39  569.18  546.84  672.29  887.55
##     LSOA11CD    LSOA01NM            WD18NM gasTCO2e_pdw beis_GBPtotal_c_gas_perdw
## 1: E01003555 Newham 007B Forest Gate South     3.622653                  887.5500
## 2: E01003532 Newham 010D    East Ham North     3.302567                  809.1289
## 3: E01003529 Newham 010A    East Ham North     3.283237                  804.3930
## 4: E01003572 Newham 008E Green Street West     3.267463                  800.5284
## 5: E01003530 Newham 010B    East Ham North     3.249362                  796.0936
## 6: E01003531 Newham 010C    East Ham North     3.240278                  793.8682
##     LSOA11CD    LSOA01NM                 WD18NM gasTCO2e_pdw beis_GBPtotal_c_gas_perdw
## 1: E01033577 Newham 037E            Royal Docks    0.3420603                  83.80478
## 2: E01033582 Newham 037H            Royal Docks    0.2892537                  70.86716
## 3: E01033576 Newham 034H     Canning Town South    0.2790662                  68.37123
## 4: E01033583 Newham 013G Stratford and New Town    0.2731223                  66.91497
## 5: E01033579 Newham 013F Stratford and New Town    0.2588745                  63.42424
## 6: E01033578 Newham 013E Stratford and New Town    0.1398151                  34.25469

Figure ?? repeats the analysis for electricity.

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity using BEIS central carbon price

Figure 4.11: £k per LSOA incurred via electricity using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity using BEIS central carbon price

Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   135.5   204.8   224.6   227.8   245.7   330.1
##     LSOA11CD    LSOA01NM            WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01003484 Newham 032B           Beckton      1.347453                   330.1261
## 2: E01033582 Newham 037H       Royal Docks      1.330567                   325.9890
## 3: E01003482 Newham 033B           Beckton      1.323440                   324.2428
## 4: E01003555 Newham 007B Forest Gate South      1.322367                   323.9800
## 5: E01003572 Newham 008E Green Street West      1.249552                   306.1403
## 6: E01003531 Newham 010C    East Ham North      1.213976                   297.4242
##     LSOA11CD    LSOA01NM                 WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01033579 Newham 013F Stratford and New Town     0.7427706                   181.9788
## 2: E01033580 Newham 037F            Royal Docks     0.7377879                   180.7580
## 3: E01033583 Newham 013G Stratford and New Town     0.7221540                   176.9277
## 4: E01003577 Newham 005C          Little Ilford     0.7203788                   176.4928
## 5: E01033576 Newham 034H     Canning Town South     0.6205552                   152.0360
## 6: E01033577 Newham 037E            Royal Docks     0.5531001                   135.5095

Figure ?? shows the same analysis for measured energy (elec + gas)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity and gas using BEIS central carbon price

Figure 4.13: £k per LSOA incurred via electricity and gas using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
£k per LSOA incurred via electricity and gas using BEIS central carbon price

Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   219.3   668.9   777.0   774.6   914.0  1211.5

4.2.3.2 Scenario 2: Rising block tariff

Applied to per dwelling values (not LSOA total) - may be methodologically dubious?

Cut at 25%, 50% - so any emissions over 50% get high carbon cost

## Cuts for total per dw
##        0%       25%       50%       75%      100% 
##  3587.624 16921.591 20949.113 24307.812 32968.220
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##            V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw beis_GBPtotal_sc2_h_perdw beis_GBPtotal_sc2_perdw
##  1: 19.850704                  2064.434                  717.6329                    0.0000                2782.067
##  2: 26.318408                  2064.434                  986.7431                 1970.5311                5021.708
##  3: 18.731128                  2064.434                  443.3368                    0.0000                2507.771
##  4: 14.702491                  1793.704                    0.0000                    0.0000                1793.704
##  5: 21.887006                  2064.434                  986.7431                  344.2065                3395.384
##  6: 28.293718                  2064.434                  986.7431                 2695.4700                5746.647
##  7: 23.829305                  2064.434                  986.7431                 1057.0302                4108.207
##  8: 12.818182                  1563.818                    0.0000                    0.0000                1563.818
##  9: 16.620137                  2027.657                    0.0000                    0.0000                2027.657
## 10:  8.355817                  1019.410                    0.0000                    0.0000                1019.410
Table 4.2: Data summary
Name …[]
Number of rows 162
Number of columns 3
Key NULL
_______________________
Column type frequency:
numeric 3
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
V1 0 1 20.46 5.65 3.59 16.92 20.95 24.31 32.97 ▁▂▇▇▂
beis_GBPtotal_sc2_perdw 0 1 3291.62 1512.63 437.69 2064.67 3052.37 4283.82 7462.19 ▃▇▅▃▁
beis_GBPtotal_sc2 0 1 2024777.26 640386.27 692179.20 1600139.17 1994368.87 2413701.30 4842460.68 ▃▇▃▁▁
##    nLSOAs sum_total_sc1 sum_total_sc2
## 1:    162      518.7582      328.0139

##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1:               1191.5493               145.36901
## 2:               1861.3599               227.08590
## 3:               1804.2023               220.11268
## 4:                643.3689                78.49101
## 5:               1780.3390               217.20136
## 6:               1341.8506               163.70577
##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw beis_GBPgas_sc2_h_perdw
## 1:               1191.5493               145.36901                       0                       0
## 2:               1861.3599               227.08590                       0                       0
## 3:               1804.2023               220.11268                       0                       0
## 4:                643.3689                78.49101                       0                       0
## 5:               1780.3390               217.20136                       0                       0
## 6:               1341.8506               163.70577                       0                       0
##    beis_GBPgas_sc2_perdw
## 1:             145.36901
## 2:             227.08590
## 3:             220.11268
## 4:              78.49101
## 5:             217.20136
## 6:             163.70577
## [1] 35.59282

## [1] 16.23055

## £m
##    nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1:    162                328.0139            35.59282             16.23055 348960
## £m
##    region nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP sumPop
## 1: London    162                328.0139            35.59282             16.23055 348960

4.2.4 Estimate retofit costs

  • from A-E <- £13,300
  • from F-G <- £26,800

Source: English Housing Survey 2018 Energy Report

Model excludes EPC A, B & C (assumes no need to upgrade)

Adding these back in would increase the cost… obvs

## To retrofit D-E (£m)
## [1] 818.9499
## Number of dwellings: 61575
## To retrofit F-G (£m)
## [1] 60.21068
## Number of dwellings: 2247
## To retrofit D-G (£m)
## [1] 879.1606
## To retrofit D-G (mean per dwelling)
## [1] 13749.17
##    meanPerLSOA_GBPm total_GBPm
## 1:         5.426917   879.1606
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.5 Compare levy with costs

4.2.5.1 Scenario 1

Totals

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Repeat per dwelling

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.5.2 Scenario 2

Totals

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Repeat per dwelling

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.2.6 Years to pay…

4.2.6.1 Scenario 1

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.689   2.297   2.690   3.074   3.312  15.648
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11.59   15.09   17.62   19.37   20.44   65.92
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Highest retofit sum cost
##      LSOA11CD    LSOA11NM                 WD18NM retrofitSum yearsToPay  epc_D_pc  epc_E_pc   epc_F_pc    epc_G_pc
##  1: E01003618 Newham 012C Stratford and New Town     9349512   19.58573 0.6254237 0.1305085 0.01864407 0.011864407
##  2: E01003495 Newham 025D                 Boleyn     8990771   15.17339 0.6325967 0.2872928 0.03867403 0.005524862
##  3: E01003539 Newham 029B         East Ham South     8598156   16.02111 0.5729443 0.2413793 0.03448276 0.013262599
##  4: E01003537 Newham 024C         East Ham South     8479391   16.60402 0.5698630 0.2520548 0.05479452 0.010958904
##  5: E01003608 Newham 028D         Plaistow South     8452678   18.45768 0.6466165 0.1829574 0.02506266 0.012531328
##  6: E01003547 Newham 007A      Forest Gate North     8380421   14.32245 0.5939675 0.2505800 0.02320186 0.006960557
##  7: E01003574 Newham 017D      Green Street West     8283186   15.63635 0.5135135 0.1912682 0.02910603 0.010395010
##  8: E01003548 Newham 006B      Forest Gate North     8233318   17.25145 0.5232143 0.1482143 0.02321429 0.007142857
##  9: E01003604 Newham 031D         Plaistow South     8092600   17.97827 0.5598527 0.1418048 0.02209945 0.012891344
## 10: E01003543 Newham 001A      Forest Gate North     8084060   17.00824 0.4574074 0.1962963 0.02037037 0.009259259

What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…

4.2.6.2 Scenario 2

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.828   3.194   4.486   5.362   6.620  31.424
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11.59   15.09   17.62   19.37   20.44   65.92
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

What happens in Year 2 totally depends on the rate of upgrades…

4.2.6.3 Compare scenarios

Comparing pay-back times for the two scenarios - who does the rising block tariff help?

x = y line shown for clarity

5 R environment

5.1 R packages used

  • base R (R Core Team 2016)
  • bookdown (Xie 2016a)
  • data.table (Dowle et al. 2015)
  • ggplot2 (Wickham 2009)
  • kableExtra (Zhu 2018)
  • knitr (Xie 2016b)
  • rmarkdown (Allaire et al. 2018)
  • skimr (Arino de la Rubia et al. 2017)

5.2 Session info

6 Data Tables

I don’t know if this will work…

## Doesn't

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.
Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.
Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.
R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.
Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.
———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.
Zhu, Hao. 2018. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.